1 Household finance David Laibson April 13, 2016

Nine claims about household finance Households: 1. Have low levels of financial literacy 2. Have very few liquid assets (live hand to mouth) 3. Have substantial illiquid wealth 4. Have a high MPC out of liquid wealth and liquidity 5. Have a low MPC out of illiquid wealth 6. Don’t choose optimal financial service products 7. Barely change their behavior after financial education interventions 8. Have misaligned financial intentions and financial actions 9. Make financial (and health) choices that are easy to manipulate

Nine claims about household finance Households: 1. Have low levels of financial literacy 2. Have very few liquid assets (live hand to mouth) 3. Have substantial illiquid wealth 4. Have a high MPC out of liquid wealth and liquidity 5. Have a low MPC out of illiquid wealth 6. Don’t choose optimal financial service products 7. Barely change their behavior after financial education interventions 8. Have misaligned financial intentions and financial actions 9. Make financial choices that are easy to manipulate

Assessing Literacy: Numeracy Compound Interest “Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?” i) More than $102; ii) Exactly $102; iii) Less than $102; iv) Don’t know (DK); v) Refuse to answer. Source: Annamarai Lusardi and Olivia Mitchell

Assessing Literacy: Inflation Inflation “Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, would you be able to buy with the money in this account:” i) More than today; ii) Exactly the same; iii) Less than today; iv) DK; v) Refuse to answer.

Assessing Literacy: Risk Diversification Risk Diversification “Do you think the following statement is true or false? Buying a single company stock usually provides a safer return than a stock mutual fund.” i) True; ii) False; iii) DK; iv) Refuse to answer.

How much do older people (ages 50+) know? 34% correctly answer all 3 questions

Correct responses: By Gender

Financial Literacy and Age

Financial Literacy and Education Source: Health and Retirement Study, 2004 Percent answering risk diversification correctly

Financial Literacy among the Young (23-27). NLSY: Percentage of correct responses  Interest rate: 79.2%  Inflation: 53.9%  Risk diversification: 46.6%

Two Takeway Points  Financial literacy should not be taken for granted  Illiteracy is widespread  Financial literacy varies a lot among demographic groups

Financial literacy matters  Financial literacy affects financial decisions: Those with low literacy:  are less likely to calculate how much they need for retirement  are less likely to participate in the stock market  are more likely to have difficulties paying off debt

More on the power of interest compounding (TNS) Suppose you owe $1,000 on your credit card and the interest rate you are charged is 20% per year compounded annually. If you didn’t pay anything off, at this interest rate, how many years would it take for the amount you owe to double ? -2 years -Under 5 years -5 to 10 years -More than 10 years -Do not know -Prefer not to answer

Payment options: Loaning money to the retailer (TNS) You purchase an appliance which costs $1,000. To pay for this appliance, you are given the following two options: a) Pay 12 monthly installments of $100 each; b) Borrow at a 20% annual interest rate and pay back $1,200 a year from now. Which is the more advantageous offer? -Option (a) -Option (b) -They are the same -Do not know -Prefer not to answer

Who has lower debt literacy? Differences between men and women Percent answering credit card question correctly or “do not know” by gender

People who make errors have “difficulties paying off debt.” 0% 5% 10% 15% 20% 25% 30% 35% Option aOption b Choose two payment options Percentage with "too much debt" Grossly underestimate compounding Gives free loan to retailer

Lusardi and Mitchell (2010) ‘I understand the stock market reasonably well’ ‘An employee of a company with publicly traded stock should have a lot of his retirement savings in the company’s stock’ ‘It is best to avoid stock of foreign companies’ ‘If the interest rate falls, bond prices will rise’ Sophisticated/correct answer to all questions: 5.8%

Lusardi and Mitchell (2010) (30% agree): ‘I understand the stock market reasonably well’ (52% disagree) ‘An employee of a company with publicly traded stock should have a lot of his retirement savings in the company’s stock’ (51% disagree): ‘It is best to avoid stock of foreign companies’ (40% agree) : ‘If the interest rate falls, bond prices will rise’ Sophisticated/correct answer to all questions: 5.8%

Fraction of people who answer “100” “If the chance of getting a disease is 10 percent, how many people out of 1,000 would be expected to get the disease?” Source: HRS; Agarwal, Driscoll, Gabaix, Laibson (2009)

Fraction of people who answer “400,000” “If 5 people all have the winning numbers in the lottery and the prize is two million dollars, how much will each of them get?” Source: HRS; Agarwal, Driscoll, Gabaix, Laibson (2009)

Nine claims about household finance Households: 1. Have low levels of financial literacy 2. Have very few liquid assets (live hand to mouth) 3. Have substantial illiquid wealth 4. Have a high MPC out of liquid wealth and liquidity 5. Have a low MPC out of illiquid wealth 6. Don’t choose optimal financial service products 7. Barely change their behavior after financial education interventions 8. Have misaligned financial intentions and financial actions 9. Make financial choices that are easy to manipulate

Households live hand to mouth Lusardi and Tufano (2009) How confident are you that you could come up with $2,000 if an unexpected need arose within the next month? –I am certain…I could –I could probably… –I probably could not… –I am certain…I could not –Do not know % 53%

24 SCF (age 65-74): In 2007, the median holding of financial assets is $68,100 HRS (age 65-69): In 2008, the median holding of financial assets is $12,500 among 1-person households HRS (age 65-69): In 2008, the median holding of financial assets is $111,600 among 2-person households

25 Survey of Consumer Finances In 2007, the median holding of financial assets including personal retirement accounts is $68, year old households

(Poterba, Venti, and Wise 2013; HRS 2008 wave)

High School education Data % with at least 1 month of income in liquid assets 42% 0.08 % with revolving credit70% mean credit card borrowing>$5000 MPC out of predictable movements in income 0.23

High Marginal Propensity to Consume Shapiro (2005): food stamps and monthly caloric cycle Mastrobuoni and Weinberg (2009): Social Security and monthly caloric cycle Parker (2014): study of 2008 Economic Stimulus Payments using Nielsen data

High MPC’s out of liquid wealth Shapiro (2005)

The data can reject a number of alternative hypotheses. Households that shop for food more frequently do not display a smaller decline in intake over the month, casting doubt on depreciation stories. Individuals in single-person households experience no less of a decline in caloric intake over the month than individuals in multi-person households. Survey respondents are not more likely to eat in another person’s home toward the end of the month. The data show no evidence of learning over time

High MPC’s out of Social security Mastrobuoni and Weinberg (2009) Individuals with substantial savings smooth consumption over the monthly pay cycle Individuals without savings consume 25 percent fewer calories the week before they receive SS checks relative to the week after

Lifecycle simulations (Angeletos et al 2001) Mortality Dependents Retirement/Social Security Three educational groups: NHS, HS, COLL Stochastic labor income Credit limit: (.30)(permanent income) 3 state variables: liquid and illiquid wealth, income. 2 choice variables: liquid and illiquid wealth investment

Preferences Constant relative risk aversion = 2 For exponential discounting economy: β=1 δ=0.94 ( match median ‘W/Y’ of 3.9 ages 50-59) For quasi-hyperbolic discounting economy: β=0.7 δ=0.96 ( match median ‘W/Y’ of 3.9 ages 50-59)

Predictions (HS education) ExponentialHyperbolicData % with at least 1 month of income in liquid assets 73%40%42% % with revolving credit19%51%70% mean credit card borrowing$900$3408>$5000 MPC out of predictable movements in income

LRT Simulation Model Stochastic Income Lifecycle variation in labor supply (e.g. retirement) Social Security system Life-cycle variation in household dependents Bequests Illiquid asset Liquid asset Credit card debt Numerical solution (backwards induction) of 90 period lifecycle problem.

Laibson, Repetto, and Tobacman (2012) Use MSM to estimate discounting parameters: –Substantial voluntarily accumulated illiquid wealth: W/Y = 3.9. –Extensive credit card borrowing: 68% didn’t pay their credit card in full last month Average credit card interest rate is 14% Credit card debt averages 13% of annual income –Consumption-income comovement: Marginal Propensity to Consume = 0.23 (i.e. consumption tracks income)

LRT Results: U t = u t +  u t+1   u t+2   u t+3    = 0.70 (s.e. 0.11)   = 0.96 (s.e. 0.01)  Null hypothesis of   = 1 rejected (t-stat of 3).  Specification test accepted.

Households have a low MPC out of illiquid wealth Because households have little liquid wealth, and the illiquid wealth is hard to access Though note that illiquid assets sometimes become liquid in large lumps (e.g., cash-out refinancing) Also, note that idiosyncratic wealth shocks to illiquid retirement savings accounts lead agents to increase their savings rate (Choi, Laibson, Madrian, Metrick 2009) –Return chasing effect

Nine claims about household finance Households: 1. Have low levels of financial literacy 2. Have very few liquid assets (live hand to mouth) 3. Have substantial illiquid wealth 4. Have a high MPC out of liquid wealth and liquidity 5. Have a low MPC out of illiquid wealth 6. Don’t choose optimal financial service products 7. Barely change their behavior after financial education interventions 8. Have misaligned financial intentions and financial actions 9. Make financial choices that are easy to manipulate

Subjects allocate $10,000 among four funds Randomly choose two subjects to receive any positive portfolio return during the subsequent year Eliminate variation in pre-fee returns Choose among S&P 500 index funds Unbundle services from returns Experimenters pay out portfolio returns, so no access to investment company services Financial illiteracy in mutual fund choice Choi, Laibson, Madrian (2011)

One year of index fund fees on a $10,000 investment

Experimental conditions Control Subjects receive only four prospectuses Prospectuses are often the only information investors receive from companies Fees transparency treatment Eliminate search costs by also distributing fee summary sheet (repeats information in prospectus) Returns treatment Highlight extraneous information by distributing summary of funds’ annualized returns since inception (repeats information in prospectus)

Fees paid by control groups (prospectus only) Minimum Possible Fee Maximum Possible Fee t-test: p= N = 83 N = 30 $443: average fee with random fund allocation 0% of College Controls put all funds in minimum-fee fund 6% of MBA Controls put all funds in minimum-fee fund

Ranking of factor importance MBA controls 1. Fees 2. 1-year performance 3. Performance since inception 4. Investment objectives 5. Desire to diversify among funds 6. Brand recognition 7. Performance over different horizon 8. Past experience with fund companies 9. Quality of prospectus 10. Customer service of fund 11. Minimum opening balance College controls 1. 1-year performance 2. Performance since inception 3. Desire to diversify among funds 4. Investment objectives 5. Quality of prospectus 6. Performance over different horizon 7. Brand recognition 8. Fees 9. Customer service of fund 10. Minimum opening balance 11. Past experience with fund companies

Effect of fee treatment (prospectus plus 1-page sheet highlighting fees) t-tests: MBA: p= College: p= N = 83 N = 30N = 29N = 85 ** 10% of College treatment put all funds in minimum-fee fund 19% of MBA treatment put all funds in minimum-fee fund

Ranking of factor importance MBA fee treatment 1. Fees 2. 1-year performance 3. Performance since inception MBA controls 1. Fees 2. 1-year performance 3. Performance since inception College fee treatment 1. Fees 2. 1-year performance 3. Performance since inception College controls 1. 1-year performance 2. Performance since inception 3. Desire to diversify among funds

Returns treatment effect on average returns since inception N = 83 N = 30N = 28N = 84 t-tests MBA: p= College: p= **

Returns treatment effect on fees N = 83 N = 30N = 28N = 84 t-tests MBA: p= College: p= **

Ranking of factor importance MBA return treatment 1. 1-year performance 2. Performance since inception 3. Fees MBA controls 1. Fees 2. 1-year performance 3. Performance since inception College return treatment 1. Performance since inception 2. 1-year performance 3. Desire to diversify among funds College controls 1. 1-year performance 2. Performance since inception 3. Desire to diversify among funds

Lack of confidence and fees (all revealed preferences are not created equal) N = 64N = 46N = 36N = 136 t-tests: MBA 1 vs. 2, p=0.2013; MBA 1 vs. 3, p=0.0479; College 1 vs. 2, p=0.2864; College 1 vs. 3, p= N = 50N = 5 *

51 We conducted one version with Harvard staff as subjects 400 subjects (administrators, faculty assistants, technical personal, but not faculty) We give every one of our subjects $10,000 and rewarded them with any gains on their investment $4,000,000 short position in stock market

52 Data from Harvard Staff Control Treatment 3% of Harvard staff in Control Treatment put all $$$ in low-cost fund $518 Fees from random allocation $431

53 Data from Harvard Staff Control Treatment Fee Treatment 3% of Harvard staff in Control Treatment put all $$$ in low-cost fund 9% of Harvard staff in Fee Treatment put all $$$ in low-cost fund $494 $518 Fees from random allocation $431

$100 bills on the sidewalk Choi, Laibson, Madrian (2009) Employer match is an instantaneous, riskless return on investment Particularly appealing if you are over 59½ years old Have the most experience, so should be savvy Retirement is close, so should be thinking about saving Can withdraw money from 401(k) without penalty We study seven companies and find that on average, half of employees over 59½ years old are not fully exploiting their employer match Average loss is 1.6% of salary per year Educational intervention has no effect

Financial education Choi, Laibson, Madrian, Metrick (2004) Seminars presented by professional financial advisors Curriculum: Setting savings goals, asset allocation, managing credit and debt, insurance against financial risks Seminars offered throughout 2000 Linked data on individual employees’ seminar attendance to administrative data on actual savings behavior before and after seminar

Effect of education is positive but small Seminar attendeesNon-attendees % planning to make change % actually made change % actually made change Those not in 401(k) Enroll in 401(k) Plan100%14%7% Those already in 401(k) Increase contribution rate28%8%5% Change fund selection47%15%10% Change asset allocation36%10%6%

57 Effect of education is positive but small Seminar attendeesNon-attendees % planning to make change % actually made change % actually made change Those not in 401(k) Enroll in 401(k) Plan100%14%7% Those already in 401(k) Increase contribution rate28%8%5% Change fund selection47%15%10% Change asset allocation36%10%6%

58 Effect of education is positive but small Seminar attendeesNon-attendees % planning to make change % actually made change % actually made change Those not in 401(k) Enroll in 401(k) Plan100%14%7% Those already in 401(k) Increase contribution rate28%8%5% Change fund selection47%15%10% Change asset allocation36%10%6%

Financial education effects are small Seminar attendees have good intentions to change their 401(k) savings behavior, but most do not follow through Financial education alone will not dramatically improve the quality of 401(k) savings outcomes Choi et al (2005) study the effect of the Enron, Worldcom, and Global Crossing scandals on employer stock holding No net sales of employer stock in reaction to these news stories These scandals did not affect the asset allocation decisions of new hires. These hires did not affect the asset allocation decisions of new hires at other Houston firms.

Information and disclosure generally don’t do much on their own Example New York City calorie disclosure (Elbel et al 2009) 60 BeforeAfter NYC (intervention city) Newark (control city) Calories from fast food purchases

How does information about your peers affect savings behavior? 61 Social marketing and peer effects Beshears, Choi, Laibson, Madrian, Milkman (2014)

Variation in peer information has no net impact on savings behavior Small perverse effects for unionized workers Small positive effect for non-unionized workers Sources of variation of peer information: Exclusion vs. inclusion of peer information Variation in peer success (due to variation in comparison group) All sources of variation generate consistent findings.

Nine claims about household finance Households: 1. Have low levels of financial literacy 2. Have very few liquid assets (live hand to mouth) 3. Have substantial illiquid wealth 4. Have a high MPC out of liquid wealth and liquidity 5. Have a low MPC out of illiquid wealth 6. Don’t choose optimal financial service products 7. Barely change their behavior after financial education interventions 8. Have misaligned financial intentions and financial actions 9. Make financial choices that are easy to manipulate

Procrastination in retirement savings Choi, Laibson, Madrian, Metrick (2002) Survey Mailed to 590 employees (random sample) 195 usable responses Matched to administrative data on actual savings behavior

66 Typical breakdown among 100 employees Out of every 100 surveyed employees 68 self-report saving too little 24 plan to raise savings rate in next 2 months 3 actually follow through

Credit card pay down Kuchler (2015) Data from on-line financial management service ReadyForZero, which gives users help in managing their debt. Median credit card debt at sign-up: $10,669. When users sign up for the site, they plan to reduce their debt significantly. Median plan over first 90 days: $1,947 Most users reduce their debt levels by very little Median pay down over first 90 days: $

Nine claims about household finance Households: 1. Have low levels of financial literacy 2. Have very few liquid assets (live hand to mouth) 3. Have substantial illiquid wealth 4. Have a high MPC out of liquid wealth and liquidity 5. Have a low MPC out of illiquid wealth 6. Don’t choose optimal financial service products 7. Barely change their behavior after financial education interventions 8. Have misaligned financial intentions and financial actions 9. Make financial choices that are easy to manipulate

Opt-in enrollment UNDESIRED BEHAVIOR: Non-participation DESIRED BEHAVIOR: participation PROCRASTINATION Opt-out enrollment (auto-enrollment) START HERE Madrian and Shea (2001); Choi, Laibson, Madrian, and Metrick (2002)

Active Choice UNDESIRED BEHAVIOR: Non-participation DESIRED BEHAVIOR: participation PROCRASTINATION START HERE Must choose for oneself Carroll, Choi, Laibson, Madrian, Metrick (2009)

UNDESIRED BEHAVIOR: Non-participation DESIRED BEHAVIOR: participation PROCRASTINATION Quick enrollment START HERE Beshears, Choi, Laibson, Madrian (2013)

UNDESIRED BEHAVIOR: Non-participation DESIRED BEHAVIOR: participation PROCRASTINATION Quick enrollment START HERE Beshears, Choi, Laibson, Madrian (2013)

73 Participation in 401K plans (for a typical firm) Default non-enrollment (opt in) 40% Quick Enrollment (“check a box”) 50% Active choice (perceived req’t to choose) 70% Default enrollment (opt out) 90% Participation Rate (1 year of tenure)

74 Gauging employee attitudes to automatic enrollment In surveys, 97% of employees in auto-enrollment firms report that they approve of auto-enrollment. Even among employees who opt out of automatic enrollment, 79% report that they approve of auto-enrollment

75 Save More Tomorrow (SMarT) Benartzi and Thaler (2004) People pre-commit to saving more in the future. Saving increases are synchronized with salary increases. People remain in the plan unless they drop out.

76 Save More Tomorrow (SMarT) Benartzi and Thaler (2004) Manufacturing firm hired a financial consultant to advise employees on how much to save Financial consultant typically advised 5% increases If participants did not accept the advice, they were offered the SMarT program

77 The First SMarT Program (cont.) Participants precommit to increase their saving rate by 3% per year Saving increases synchronized with pay raises The increases continue unless the participant opts out or hits the plan max

78 Participation Data

79 Saving Rates Source: Brian Tarbox

Lessons from SMarT Throw the kitchen sink at the problem. Upside: likely to work Downside: not sure what things matter Subsequent studies reveal that linking to pay raises is nice but not essential. Ease of sign up is essential.

Should Choice Architecture Influence Economic Outcomes? Standard neoclassical theory: If transactions costs are small and stakes are large, choice architecture should not influence rational consumers. In practice, defaults make an enormous difference: Organ donation (Johnson and Goldstein 2003) Car insurance Car purchase options Consent to receive marketing Savings Asset allocation

Outline 1. Seemingly minor nudges affect all saving and asset allocation outcomes 2. At least four psychological factors jointly contribute to these effects i. Financial illiteracy ii. Endorsement iii. Complexity and transaction costs (bounded rationality) iv. Present-bias v. (Status quo bias/Loss Aversion) 3. The surprising weakness of incentive- and information-based interventions 4. Extensions to health domain

1. Seemingly minor nudges affect saving and asset allocation i. Participation ii. Contribution rates iii. Asset allocation iv. Pre-retirement distributions v. Decumulation / annuitization

Participation, Contribution rates, and Asset Allocation Automatic Enrollment in a US 401(k) plan Welcome to the company If you don’t do anything… You are automatically enrolled in the 401(k) You save 2% of your pay Call this phone number to opt out of enrollment or change your investment allocations First study: Madrian and Shea (2001)

Defaults and Participation Beshears, Choi, Laibson, and Madrian (2010)

Default Stickiness

Choi, Laibson, Madrian, Metrick (2004) Hired before AE Hired after AE ended Hired during AE

Employees enrolled under auto-enrollment cluster at the default contribution rate. Default contribution rate under automatic enrollment

Participants stay at the automatic enrollment defaults for a long time.

Automatic enrollment: Summary Automatic enrollment dramatically increases 401(k) participation Participants hired under automatic enrollment tend to stay at the automatic enrollment defaults Similar default effects are observed for auto-escalation otherwise known as SMART (Save More Tomorrow; Benartzi and Thaler, 2004) Has two flavors: opt-out auto-escalation and opt-in auto-escalation (it’s the opt-out version that works – very few opt-in on their own) Also observe default effects for… cash distributions at separation saving rates at changing match thresholds

91 Gauging employee attitudes to automatic enrollment In firms with standard 401(k) plans (no auto-enrollment), 2/3 of workers say that they should save more Opt-out rates under automatic enrollment are typically only 5%-10% (opt-out rates rarely exceed 20%) Under automatic enrollment employers report “no complaints” in 401(k) plans In surveys, 97% of employees in auto-enrollment firms report that they approve of auto-enrollment. Even among employees who opt out of automatic enrollment, 79% report that they approve of auto-enrollment The US government has adopted automatic enrollment.

Additional evidence on Asset Allocation Private account component of Swedish Social Security system (Cronqvist and Thaler, 2004) At inception, one-third of assets are invested in the default fund Subsequent enrollees invest 90% of assets in the default fund Company match in employer stock (Choi, Laibson and Madrian 2009)

Active Choice UNDESIRED BEHAVIOR: Non-participation DESIRED BEHAVIOR: participation PROCRASTINATION START HERE Must choose for oneself

The Flypaper Effect in Individual Investor Asset Allocation (Choi, Laibson, Madrian 2009) Studied a firm that used several different match systems in their 401(k) plan. I’ll discuss two of those regimes today: Match allocated to employer stock and workers can reallocate Call this “default” case (default is employer stock) Match allocated to an asset actively chosen by workers; workers required to make an active designation. Call this “active choice” case (workers must choose) Economically, these two systems are identical. They both allow workers to do whatever the worker wants.

95 Consequences of the two regimes Match Defaults into Employer Stock Active choice Own Balance in Employer Stock 24%20% Matching Balance in Employer Stock 94%27% Total Balance in Employer Stock 56%22% Balances in employer stock

Cash Distributions What happens to savings plan balances when employees leave their jobs? Employees can request a cash distribution or roll balances over into another account Balances >$5000: default leaves balances with former employer Balances <$5000: default distributes balances as cash transfer Vast majority of employees accept default (Choi et al. 2002, 2004a and 2004b) When employees receive small cash distributions, balances typically consumed (Poterba, Venti and Wise 1998)

Post-Retirement Distributions Social Security Joint and survivor annuity (reduced benefits) Defined benefit pension Annuity Lump sum payout if offered Defined contribution savings plan Lump sum payout Annuity if offered

Defined Benefit Pension Annuitization Annuity income and economic welfare of the elderly Social Security replacement rate relatively low on average 17% of women fall into poverty after the death of their spouse (Holden and Zick 2000) For married individuals, three distinct annuitization regimes Pre-1974: no regulation ERISA I (1974): default joint-and-survivor annuity with option to opt-out ERISA II (1984 amendment): default joint-and-survivor annuity, opting out required notarized permission of spouse

Defined Benefit Pension Annuitization Effect of joint-and-survivor default on annuitization Pre-1974: Less than half of married men have joint-and-survivor annuity Post-ERISA (I + II): joint-and-survivor annuitization increases 25 percentage points (Holden and Nicholson 1998) Post-1984 amendments: joint-and-survivor annuitization increases 5 to 10 percentage points (Saku 2001)

However, there are limits to defaults Many/most households opt out of Defined Benefit annuitization (see Previterro 2010) Most households generally opt out of aggressive defaults (Beshears, Choi, Laibson, and Madrian 2010) Low income households are stickier than high income households (Beshears, Choi, Laibson, Madrian, and Wang 2016). 100

Plan Details (Beshears et al 2010) Large UK firm Employees eligible for DC plan upon hire Minimum employee contribution rate 4%, with one-for-one employer match on contributions between 12% and 18% Immediate automatic enrollment at 12% Study new hires, March 2006 – June 2007

Opting Out of Default Contribution Rate N = 900

Opting Out of Default Contribution Rate

i. Low levels of financial literacy ii. Endorsement iii. Complexity and transaction costs iv. Present-bias 2. Four mutually compatible psychological factors contribute to default effects

Who Stays at the Default? Beshears, Choi, Laibson, and Madrian (2010)

Sub-population that opts out Beshears, Choi, Laibson, and Madrian (2010)

Everyone at company including those at 12% default Beshears, Choi, Laibson, and Madrian (2010)

More data on financial illiteracy John Hancock Financial Services Defined Contribution Plan Survey (2002) 38% of respondents report that they have little or no financial knowledge 40% of respondents believe that a money market fund contains stocks Two-thirds of respondents don’t know that it is possible to lose money in government bonds Respondents on average believe that employer stock is less risky than a stock mutual fund Two-thirds report that they would be better off working with an investment advisor than managing investments solo

Lusardi and Mitchell (2010) ‘I understand the stock market reasonably well’ ‘An employee of a company with publicly traded stock should have a lot of his retirement savings in the company’s stock’ ‘It is best to avoid stock of foreign companies’ ‘If the interest rate falls, bond prices will rise’

Lusardi and Mitchell (2010) (30% agree): ‘I understand the stock market reasonably well’ (52% disagree) ‘An employee of a company with publicly traded stock should have a lot of his retirement savings in the company’s stock’ (51% disagree): ‘It is best to avoid stock of foreign companies’ (40% agree) : ‘If the interest rate falls, bond prices will rise’ Sophisticated/correct answer to all questions: 5.8%

Subjects allocate $10,000 among four funds Randomly choose two subjects to receive any positive portfolio return during the subsequent year Eliminate variation in pre-fee returns Choose among S&P 500 index funds Unbundle services from returns Experimenters pay out portfolio returns, so no access to investment company services Financial illiteracy in mutual fund choice Choi, Laibson, Madrian (2011)

One year of index fund fees on a $10,000 investment

Experimental conditions Control Subjects receive only four prospectuses Prospectuses are often the only information investors receive from companies Fees transparency treatment Eliminate search costs by also distributing fee summary sheet (repeats information in prospectus) Returns treatment Highlight extraneous information by distributing summary of funds’ annualized returns since inception (repeats information in prospectus)

Fees paid by control groups (prospectus only) Minimum Possible Fee Maximum Possible Fee t-test: p= N = 83 N = 30 $443: average fee with random fund allocation 0% of College Controls put all funds in minimum-fee fund 6% of MBA Controls put all funds in minimum-fee fund

Ranking of factor importance MBA controls 1. Fees 2. 1-year performance 3. Performance since inception 4. Investment objectives 5. Desire to diversify among funds 6. Brand recognition 7. Performance over different horizon 8. Past experience with fund companies 9. Quality of prospectus 10. Customer service of fund 11. Minimum opening balance College controls 1. 1-year performance 2. Performance since inception 3. Desire to diversify among funds 4. Investment objectives 5. Quality of prospectus 6. Performance over different horizon 7. Brand recognition 8. Fees 9. Customer service of fund 10. Minimum opening balance 11. Past experience with fund companies

Effect of fee treatment (prospectus plus 1-page sheet highlighting fees) t-tests: MBA: p= College: p= N = 83 N = 30N = 29N = 85 ** 10% of College treatment put all funds in minimum-fee fund 19% of MBA treatment put all funds in minimum-fee fund

Ranking of factor importance MBA fee treatment 1. Fees 2. 1-year performance 3. Performance since inception MBA controls 1. Fees 2. 1-year performance 3. Performance since inception College fee treatment 1. Fees 2. 1-year performance 3. Performance since inception College controls 1. 1-year performance 2. Performance since inception 3. Desire to diversify among funds

Returns treatment effect on average returns since inception N = 83 N = 30N = 28N = 84 t-tests MBA: p= College: p= **

Returns treatment effect on fees N = 83 N = 30N = 28N = 84 t-tests MBA: p= College: p= **

Ranking of factor importance MBA return treatment 1. 1-year performance 2. Performance since inception 3. Fees MBA controls 1. Fees 2. 1-year performance 3. Performance since inception College return treatment 1. Performance since inception 2. 1-year performance 3. Desire to diversify among funds College controls 1. 1-year performance 2. Performance since inception 3. Desire to diversify among funds

Lack of confidence and fees (all revealed preferences are not created equal) N = 64N = 46N = 36N = 136 t-tests: MBA 1 vs. 2, p=0.2013; MBA 1 vs. 3, p=0.0479; College 1 vs. 2, p=0.2864; College 1 vs. 3, p= N = 50N = 5 *

122 We conducted one version with Harvard staff as subjects 400 subjects (administrators, faculty assistants, technical personal, but not faculty) We give every one of our subjects $10,000 and rewarded them with any gains on their investment $4,000,000 short position in stock market

123 Data from Harvard Staff Control Treatment 3% of Harvard staff in Control Treatment put all $$$ in low-cost fund $518 Fees from random allocation $431

124 Data from Harvard Staff Control Treatment Fee Treatment 3% of Harvard staff in Control Treatment put all $$$ in low-cost fund 9% of Harvard staff in Fee Treatment put all $$$ in low-cost fund $494 $518 Fees from random allocation $431

Fraction of people who answer “100” “If the chance of getting a disease is 10 percent, how many people out of 1,000 would be expected to get the disease?” Source: HRS; Agarwal, Driscoll, Gabaix, Laibson (2009)

Fraction of people who answer “400,000” “If 5 people all have the winning numbers in the lottery and the prize is two million dollars, how much will each of them get?” Source: HRS; Agarwal, Driscoll, Gabaix, Laibson (2009)

ii. Endorsement A non-zero default is perceived as advice Evidence Elective employee stock allocation in firms that do and do not match in employer stock (Benartzi 2001, Holden and Vanderhei 2001, and Brown, Liang and Weisbenner 2006) Asset allocation of employees hired before automatic enrollment (Choi, Laibson, Madrian 2006)

Asset Allocation Outcomes of Employees Who are Not Subject to Automatic Enrollment Any balances in default fund All balances in default fund Company D Hired before AE, participated before AE13%2% Choi, Laibson, and Madrian (2007)

Asset Allocation Outcomes of Employees Who are Not Subject to Automatic Enrollment Any balances in default fund All balances in default fund Company D Hired before, participated before AE13%2% Hired before, participated after AE29%16% Choi, Laibson, and Madrian (2007)

iii. Complexity Complexity  delay Psychology literature (Tversky and Shafir 1992, Shafir, Simonson and Tversky 1993, Dhar and Knowlis 1999, Iyengar and Lepper 2000 ) Savings literature: each additional 10 funds produces a 1.5 to 2.0 percentage point decline in participation (Iyengar, Huberman and Jiang 2004) Also results on complexity generating more conservative asset allocation (Iyengar and Kamenica 2007). Quick enrollment experiments

UNDESIRED BEHAVIOR: Non-participation DESIRED BEHAVIOR: participation PROCRASTINATION Quick enrollment START HERE

UNDESIRED BEHAVIOR: Non-participation DESIRED BEHAVIOR: participation PROCRASTINATION Quick enrollment START HERE

Complexity and Quick Enrollment Conceptual Idea Simplify the savings plan enrollment decision by giving employees an easy way to elect a pre-selected contribution rate and asset allocation bundle Implementation at Company D New hires at employee orientation: 2% contribution rate invested 50% money market & 50% stable value Implementation at Company E Existing non-participants: 3% contribution rate invested 100% in money market fund

134 Complexity and Quick Enrollment Conceptual Idea Simplify the savings plan enrollment decision by giving employees an easy way to elect a pre-selected contribution rate and asset allocation bundle Implementation at Company D New hires at employee orientation: 2% contribution rate invested 50% money market / 50% stable value Existing non-participants: employee selects contribution rate invested 50% money market / 50% stable value Implementation at Company E Existing non-participants: 3% contribution rate invested 100% in money market fund

Quick Enrollment and Savings Plan Participation

Simplified enrollment raises participation Beshears, Choi, Laibson, Madrian (2006)

iv. Present-Biased Preferences Self control and savings outcomes: Why do today what you can put off until tomorrow? (Akerlof 1991, Laibson 1997; O’Donoghue and Rabin 1999; Diamond and Koszegi 2003, Gruber and Koszegi 2003, Della Vigna and Malmendier 2004, 2006) Framework: exponential discounting with an additional factor, β<1, that uniformly down-weights the future. U t = u t +  u t+1   u t+2   u t+3 

Lusardi and Tufano (2009) How confident are you that you could come up with $2,000 if an unexpected need arose within the next month? I am certain…I could I could probably… I probably could not… I am certain…I could not Do not know % 53%

139 SCF (age 65-74): In 2007, the median holding of financial assets is $68,100 HRS (age 65-69): In 2008, the median holding of financial assets is $12,500 among 1-person households HRS (age 65-69): In 2008, the median holding of financial assets is $111,600 among 2-person households

140 SCF: In 2007, the median holding of financial assets is $68,100 HRS: In 2008, the median holding of financial assets is $12,500 among 1-person households HRS: In 2008, the median holding of financial assets is $111,600 among 2-person households year old households

Laibson, Repetto, and Tobacman (2004) Use MSM to estimate discounting parameters: Substantial illiquid retirement wealth: W/Y = 3.9. Extensive credit card borrowing: 68% didn’t pay their credit card in full last month Average credit card interest rate is 14% Credit card debt averages 13% of annual income Consumption-income comovement: Marginal Propensity to Consume = 0.25 (i.e. consumption tracks income)

LRT Simulation Model Stochastic Income Lifecycle variation in labor supply (e.g. retirement) Social Security system Life-cycle variation in household dependents Bequests Illiquid asset Liquid asset Credit card debt Numerical solution (backwards induction) of 90 period lifecycle problem.

LRT Results: U t = u t +  u t+1   u t+2   u t+3   = 0.70 (s.e. 0.11)  = 0.96 (s.e. 0.01) Null hypothesis of  = 1 rejected (t-stat of 3). Specification test accepted. Moments: Empirical Simulated (Hyperbolic) %Visa: 68%63% Visa/Y: 13%17% MPC: 23%31% f(W/Y):

Intuition Short-run discount rate: Long-run discount rate: 144

Procrastination (assume  ½,  = 1). Suppose you can join the plan today (effort cost $50) to gain delayed benefits $20,000 (e.g. value of match) Every day you delay, total benefits fall by $10. What are the discounted costs of joining at different periods? Join Today: ½ [0] = -50 Join t+1: 0 + ½ [ ] = -30 Join t+2: 0 + ½ [ ] = -35 Join t+3: 0 + ½ [ ] = -40

146 Interaction with financial illiteracy Consider someone with a high level of financial literacy, so effort cost is only $5 (not $50) As before, every day of delay, total benefits fall by $10. What are the discounted costs of joining at different periods? Join Today: -5 + ½ [0] = -5 Join t+1: 0 + ½ [ ] = -7.5 Join t+2: 0 + ½ [ ] = Join t+3: 0 + ½ [ ] = -17.5

147 Interaction with endorsement and complexity Consider a plan with a simple form, or an endorsed form, so the effort cost is again only $5 (not $50) As before, every period of delay, total benefits fall by $10. What are the discounted costs of joining at different periods? Join Today: -5 + ½ [0] = -5 Join t+1: 0 + ½ [ ] = -7.5 Join t+2: 0 + ½ [ ] = Join t+3: 0 + ½ [ ] = -17.5

Procrastination in retirement savings Choi, Laibson, Madrian, Metrick (2002) Survey Mailed to 590 employees (random sample) 195 usable responses Matched to administrative data on actual savings behavior

149 Typical breakdown among 100 employees Out of every 100 surveyed employees 68 self-report saving too little 24 plan to raise savings rate in next 2 months 3 actually follow through

3. Alternative Policies Paying employees to save Education/disclosure Peer marketing

$100 bills on the sidewalk Choi, Laibson, Madrian (2009) Employer match is an instantaneous, riskless return on investment Particularly appealing if you are over 59½ years old Have the most experience, so should be savvy Retirement is close, so should be thinking about saving Can withdraw money from 401(k) without penalty We study seven companies and find that on average, half of employees over 59½ years old are not fully exploiting their employer match Average loss is 1.6% of salary per year Educational intervention has no effect

Financial education Choi, Laibson, Madrian, Metrick (2004) Seminars presented by professional financial advisors Curriculum: Setting savings goals, asset allocation, managing credit and debt, insurance against financial risks Seminars offered throughout 2000 Linked data on individual employees’ seminar attendance to administrative data on actual savings behavior before and after seminar

Effect of education is positive but small Seminar attendeesNon-attendees % planning to make change % actually made change % actually made change Those not in 401(k) Enroll in 401(k) Plan100%14%7% Those already in 401(k) Increase contribution rate28%8%5% Change fund selection47%15%10% Change asset allocation36%10%6%

154 Effect of education is positive but small Seminar attendeesNon-attendees % planning to make change % actually made change % actually made change Those not in 401(k) Enroll in 401(k) Plan100%14%7% Those already in 401(k) Increase contribution rate28%8%5% Change fund selection47%15%10% Change asset allocation36%10%6%

155 Effect of education is positive but small Seminar attendeesNon-attendees % planning to make change % actually made change % actually made change Those not in 401(k) Enroll in 401(k) Plan100%14%7% Those already in 401(k) Increase contribution rate28%8%5% Change fund selection47%15%10% Change asset allocation36%10%6%

Financial education effects are small Seminar attendees have good intentions to change their 401(k) savings behavior, but most do not follow through Financial education alone will not dramatically improve the quality of 401(k) savings outcomes Choi et al (2005) study the effect of the Enron, Worldcom, and Global Crossing scandals on employer stock holding No net sales of employer stock in reaction to these news stories These scandals did not affect the asset allocation decisions of new hires. These hires did not affect the asset allocation decisions of new hires at other Houston firms.

Information and disclosure generally don’t do much on their own Example New York City calorie disclosure (Elbel et al 2009) 157 BeforeAfter NYC (intervention city) Newark (control city) Calories from fast food purchases

How does information about your peers affect savings behavior? 158 Social marketing and peer effects Beshears, Choi, Laibson, Madrian, Milkman (2011)

Variation in peer information has no net impact on savings behavior Small perverse effects for unionized workers Small positive effect for non-unionized workers Sources of variation of peer information: Exclusion vs. inclusion of peer information Variation in peer success (due to variation in comparison group) All sources of variation generate consistent findings.

Behavioral mechanism design 1. Specify a social welfare function (not necessarily based on revealed preference) 2. Specify a theory of consumer/firm behavior (consumers and/or firms may not behave optimally). 3. Solve for the institutional structure that maximizes the social welfare function, conditional on the theory of consumer/firm behavior.

1. Specific example of behavioral mechanism design: optimal defaults 2. Empirical evidence on active choice 162

163 A specific example: Optimal Defaults – public policy Mechanism design problem in which policy makers set a default for agents with present bias ( Carrol, Choi, Laibson, Madrian and Metrick 2009 )

164 Basic set-up of problem Specify (dynamically consistent) social welfare function of planner (e.g., set β=1) Specify behavioral model of households Flow cost of staying at the default Effort cost of opting-out of the default Effort cost varies over time  option value of waiting to leave the default Present-biased preferences  procrastination Planner picks default to optimize social welfare function

Specific Details Agent needs to do a task (once). –Switch savings rate, s, from default, d, to optimal savings rate, Until task is done, agent losses per period. Doing task costs c units of effort now. –Think of c as opportunity cost of time Each period c is drawn from a uniform distribution on [0,1]. Agent has present-biased discount function with β < 1 and δ = 1. So discount function is: 1, β, β, β, … Agent has sophisticated (rational) forecast of her own future behavior. She knows that next period, she will again have the weighting function 1, β, β, β, …

Timing of game 1.Period begins (assume task not yet done) 2.Pay cost θ (since task not yet done) 3.Observe current value of opportunity cost c (drawn from uniform distribution) 4.Do task this period or choose to delay again? 5.It task is done, game ends. 6.If task remains undone, next period starts. Period t-1Period tPeriod t+1 Pay cost θObserve current value of c Do task or delay again

Sophisticated procrastination There are many equilibria of this game. Let’s study the equilibrium in which sophisticates act whenever c < c *. We need to solve for c *. Let V represent the expected undiscounted cost if the agent decides not to do the task at the end of the current period t: Cost you’ll pay for certain in t+1, since job not yet done Likelihood of doing it in t+1 Expected cost conditional on drawing a low enough c * so that you do it in t+1 Likelihood of not doing it in t+1 Expected cost starting in t+2 if project was not done in t+1

In equilibrium, the sophisticate needs to be exactly indifferent between acting now and waiting. Solving for c *, we find: Expected delay is:

How does introducing β < 1 change the expected delay time? If β=2/3, then the delay time is scaled up by a factor of In other words, it takes times longer than it “should” to finish the project

V vs. V hat

A model of procrastination: naifs Same assumptions as before, but… Agent has naive forecasts of her own future behavior. She thinks that future selves will act as if β = 1. So she (mistakenly) thinks that future selves will pick an action threshold of

In equilibrium, the naif needs to be exactly indifferent between acting now and waiting. To solve for V, recall that:

Substituting in for V : So the naif uses an action threshold (today) of But anticipates that in the future, she will use a higher threshold of

So her (naïve) forecast of delay is: And her actual delay will be: Being naïve, scales up her delay time by an additional factor of 1/β.

Summary If β = 1, expected delay is If β < 1 and sophisticated, expected delay is If β < 1 and naïve, anticipated delay is If β < 1 and naïve, true delay is

That completes theory of consumer behavior. Now solve for government’s optimal policy. Now we need to solve for the optimal default, d. Note that the government’s objective ignores present bias, since it uses V as the welfare criterion.

178 Optimal ‘Defaults’ Two classes of optimal defaults emerge from this calculation Automatic enrollment Optimal when employees have relatively homogeneous savings preferences (e.g. match threshold) and relatively little propensity to procrastinate “Active Choice” — require individuals to make a choice (eliminate the option to passively accept a default) Optimal when employees have relatively heterogeneous savings preferences and relatively strong tendency to procrastinate Key point: sometimes the best default is no default.

Preference Heterogeneity 1 0 Beta Active Choice Center Default Offset Default 30% 0% Low Heterogeneity High Heterogeneity

180 Lessons from theoretical analysis of defaults Defaults should be set to maximize average well- being, which is not the same as saying that the default should be equal to the average preference. Endogenous opting out should be taken into account when calculating the optimal default. The default has two roles: causing some people to opt out of the default (which generates costs and benefits) implicitly setting savings policies for everyone who sticks with the default

181 Empirical evidence on active choice Carroll, Choi, Laibson, Madrian, Metrick (2009) Active choice mechanisms require employees to make an active choice about 401(k) participation. Welcome to the company You are required to submit this form within 30 days of hire, regardless of your 401(k) participation choice If you don’t want to participate, indicate that decision If you want to participate, indicate your contribution rate and asset allocation Being passive is not an option

182

183 Active choice in 401(k) plans Active decision raises 401(k) participation. Active decision raises average savings rate by 50 percent. Active decision doesn’t induce choice clustering. Under active decision, employees choose savings rates that they otherwise would have taken three years to achieve. (Average level as well as the multivariate covariance structure.)

Other active choice interventions Active choice in asset allocation (Choi, Laibson, and Madrian 2009) Active choice in home delivery of chronic medications (Beshears, Choi, Laibson, Madrian 2011) 184

Regulatory use of defaults: Card Act Agarwal, Chomsisengphet, Mahoney, Stroebel (2014) 185 CARD Act: signed into law on May 22, Primarily amended the Truth in Lending Act and instituted a number of new protection and disclosure requirements for credit cards. The regulation excluded small business credits cards. The provisions of the CARD Act were scheduled to take effect in three phases between August 20, 2009, and August 22, 2010.

Card Act: Phase 1 – August 20, 2009: Required banks to provide consumers with 45-day advance notice of rate increases or any other significant changes to terms and conditions. Required lenders to (i) inform consumers in the same notice of their right to cancel the credit card account before the increase or change goes into effect and (ii) mail or deliver periodic statements for credit cards at least 21 days before payment is due. 186

Card Act: Phase 2 – February 22, 2010: No fees could be imposed for making a transaction that would put the account over its credit limit unless the cardholder explicitly “opts in” for the credit card company to process rather than decline over-limit transactions. Furthermore, an over limit fee may be imposed only once during the billing cycle in which the limit is exceeded. Required statements to display: Number of months and total cost to the consumer (principal and interest) that it would take to pay the outstanding balance, if the consumer pays only the required minimum payments; The monthly payment amount that would eliminate the outstanding balance in 36 months and the total cost to the consumer, including interest and principal. 187

Card Act: Phase 3 – August 22, 2010: Regulated the fees banks can charge by requiring them to be “reasonable and proportional.” Cannot charge a late fee of more than $25 unless one of the last six payments was late (in which case the fee may be $35). Late fee cannot be larger than the minimum payment. Over limit fees capped at the actual over limit amount. Prevented issuers from charging more than one penalty fee per violation in a single billing period. Prohibited the charging of inactivity fees for not using the credit card for a period of time. Required lenders to re-evaluate any new rate increases every six months. 188

189 Regulatory use of defaults: Card Act Agarwal, Chomsisengphet, Mahoney, Stroebel (2014)

190 Agarwal, Chomsisengphet, Mahoney, Stroebel (2014)

Households receive bad advice See audit studies by: Anagol, Cole, and Sarkar (2013) Mullainathan, Noth, and Schoar (2010) 191

192 Translation to the health domain Similarities with saving behavior: Individuals and society have aligned goals – Improve health and control costs Individuals want behavior change (just not right now) – Improve diet – Increase physical activity – Stop smoking – Adhere to therapeutic recommendations – Utilize wellness programs

Information and disclosure generally don’t do much on their own Example New York City calorie disclosure (Elbel et al 2009) 193 BeforeAfter NYC (intervention city) Newark (control city) Calories from fast food purchases

194 Flu shot study: Control Condition Employees informed of the dates/times of workplace flu clinics

195 Flu Shot Study: Date Plan Condition Employees invited to choose a concrete DATE for getting a flu vaccine Employees informed of the dates/times of workplace flu clinics

Date/Time Plan Condition Employees invited to choose a concrete DATE AND TIME for getting a flu vaccine Employees informed of the dates/times of workplace flu clinics

Flu shot adherence Milkman, Beshears, Choi, Laibson, and Madrian 2011 Flu shot letter 33.0% Flu shot letter + date plan 34.6% Flu shot letter + date plan + time plan 37.2%

Use Active Choice to encourage adoption of Home Delivery of chronic medication Beshears, Choi, Laibson, and Madrian (in preparation) Voluntary No plan design change Lower employee co-pay Time saving for employee Lower employer cost Better medication adherence Improved safety Member Express Scripts Scientific Advisory Board (Payments donated to charity by Express Scripts.)

Home Delivery Utilization for All Drug Clas ses Beshears, Choi, Laibson, Madrian (2012) %

Results from pilot study on 54,863 employees without home delivery taking chronic medication Among those making an active choice: Fraction choosing home delivery: 52.2% Fraction choosing standard pharmacy pick-up: 47.8%

Results from pilot study at one company * Annualized Rxs by Mail* Before After Annual Savings at pilot company Plan $350,000+ Members $820,000+ Total Savings $1,170,000+

Outline 1.Seemingly minor nudges affect behavior 2.At least four psychological factors jointly contribute to these effects i.Financial illiteracy ii.Endorsement iii.Complexity and transaction costs iv.Present-bias v.(Status quo bias/Loss Aversion) 3.The surprising weakness of incentive- and information-based interventions 4.Extensions to health domain

Nine claims about household finance Households: 1.Have low levels of financial literacy 2.Have very few liquid assets (live hand to mouth) 3.Have substantial illiquid wealth 4.Have a high MPC out of liquid wealth and liquidity 5.Have a low MPC out of illiquid wealth 6.Don’t choose optimal financial service products 7.Barely change their behavior after financial education interventions 8.Have misaligned financial intentions and financial actions 9.Make financial choices that are easy to manipulate